Not all rushing yards are equal
Traditional stats miss the “how” behind yards gained
Can we “measure” effort using tracking data?
No universal way to evaluate effort
Game, play, player, tracking data from Weeks 1-9 1
Running plays where a running back (RB) is the ball carrier
Trimmed each play to frames between handoff and end of play
Backed out key features, aggregated per-play and per-player:
Regression and correlation to determine which metric(s) align best with high impact plays
Residual effort estimation: predict minimum effort needed to avoid a tackle and compare to actual effort to measure excess (“residual”) effort
Logistic regression, random forest, etc to predict likelihood of tackle broken or play success from effort and related metrics
Clustering to classify RBs by effort-reward profile and identify archetypes of RB behavior
Define research question and scope ✅
Data cleaning and preprocessing, EDA ✅
Select effort metric(s)
Use chosen metric to answer questions related to effort (residual effort, tackle outcome probabilities, minimum required effort per play, etc)
Interpret results, complete report and presentation